# heucoder/dimensionality_reduction_alo_codes

**Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/heucoder-dimensionality-reduction-alo-codes).**

_How this analysis was created: the description and tags below were written by an AI model that read this project's README and public documentation pages; stars, license and language come straight from the GitHub API. The model does not read the source code._

2,374 stars · 609 forks · Python · Apache-2.0

## Links

- GitHub: https://github.com/heucoder/dimensionality_reduction_alo_codes
- awesome-repositories: https://awesome-repositories.com/repository/heucoder-dimensionality-reduction-alo-codes.md

## Topics

`data-reduction` `feature-extraction` `python`

## Description

Dimensionality reduction alo codes is a Python collection of implementations for classic data reduction algorithms, functioning as a machine learning feature extraction tool. It transforms complex high-dimensional datasets into lower-dimensional spaces through techniques that retain maximum variance, maximize class separability, and translate proximities between data points into spatial distances. 

The library includes capabilities for eigenvalue variance maximization, linear discriminant projection, nonlinear neighborhood preserving embedding, singular value matrix decomposition, and statistical independent component separation. These operations extract meaningful patterns from datasets, decompose matrices to uncover latent structures, and process multivariate signals to identify hidden source components in data.

## Tags

### Scientific & Mathematical Computing

- [Dimensionality Reduction](https://awesome-repositories.com/f/scientific-mathematical-computing/dimensionality-reduction.md) — Transforms complex high-dimensional datasets into lower-dimensional spaces using classic mathematical techniques.
- [Eigenvalue Computations](https://awesome-repositories.com/f/scientific-mathematical-computing/eigenvalue-computations.md) — Calculates eigenvalues and eigenvectors of data covariance matrices to extract principal axes of maximum variance.
- [Principal Component Analysis](https://awesome-repositories.com/f/scientific-mathematical-computing/linear-algebra-routines/principal-component-analysis.md) — Transforms high-dimensional datasets into lower-dimensional spaces while retaining maximum variance.
- [Matrix Decompositions](https://awesome-repositories.com/f/scientific-mathematical-computing/matrix-decompositions.md) — Decomposes data matrices into constituent components to extract principal variance channels.
- [SDR Signal Analysis](https://awesome-repositories.com/f/scientific-mathematical-computing/data-modeling-processing/signal-processing/sdr-signal-analysis.md) — Separates multivariate signals into statistically independent sub-components to identify hidden source signals.
- [Matrix Factorization Toolkits](https://awesome-repositories.com/f/scientific-mathematical-computing/matrix-factorization-toolkits.md) — Decomposes matrices into constituent components to uncover latent structures and reduce complexity.

### Artificial Intelligence & ML

- [Neighborhood Preserving Embeddings](https://awesome-repositories.com/f/artificial-intelligence-ml/dimensionality-reduction/locally-linear-embeddings/neighborhood-preserving-embeddings.md) — Maps complex high-dimensional manifolds into lower-dimensional spaces while preserving local geometric distances.
- [Feature Extraction](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-extraction.md) — Provides a toolkit for extracting meaningful patterns and significant variance from complex datasets.
- [Independent Component Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/independent-component-analysis.md) — Separates multivariate signals into statistically independent sub-components by maximizing non-Gaussianity.
- [Linear Discriminant Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-classification/dimensionality-reduction/linear-discriminant-analysis.md) — Projects high-dimensional samples onto lower-dimensional subspaces to maximize between-class separability.
- [Dimensionality Reduction](https://awesome-repositories.com/f/artificial-intelligence-ml/supervised-classification/dimensionality-reduction.md) — Transforms high-dimensional datasets into lower-dimensional spaces using classic reduction algorithms. ([source](https://github.com/heucoder/dimensionality_reduction_alo_codes#readme))
- [Principal Component Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-similarity-search/fixed-dimension-projections/principal-component-analysis.md) — Projects high-dimensional datasets into lower-dimensional spaces using classic mathematical techniques. ([source](https://github.com/heucoder/dimensionality_reduction_alo_codes/blob/master/README.md))

### Data & Databases

- [Multidimensional Scaling](https://awesome-repositories.com/f/data-databases/vector-quantization/high-dimensional-vector-compressors/dimensionality-reduction/multidimensional-scaling.md) — Translates proximities between data points into spatial distances in low-dimensional spaces.

### Education & Learning Resources

- [Dimensionality Reduction Tools](https://awesome-repositories.com/f/education-learning-resources/principal-component-analysis-tutorials/dimensionality-reduction-tools.md) — Provides a collection of Python implementations for classic data reduction algorithms.